NTGST-Based Parallel Computer Vision Inspection for High Resolution BLU

NTGST 병렬화를 이용한 고해상도 BLU 검사의 고속화

  • 김복만 (경북대학교 전자전기컴퓨터학부) ;
  • 서경석 (경북대학교 전자전기컴퓨터학부) ;
  • 최흥문 (경북대학교 전자전기컴퓨터학부)
  • Published : 2004.11.01

Abstract

A novel fast parallel NTGST is proposed for high resolution computer vision inspection of the BLUs in a LCD production line. The conventional computation- intensive NTGST algorithm is modified and its C codes are optimized into fast NTGST to be adapted to the SIMD parallel architecture. And then, the input inspection image is partitioned and allocated to each of the P processors in multi-threaded implementation, and the NTGST is executed on SIMD architecture of N data items simultaneously in each thread. Thus, the proposed inspection system can achieve the speedup of O(NP). Experiments using Dual-Pentium III processor with its MMX and extended MMX SIMD technology show that the proposed parallel NTGST is about Sp=8 times faster than the conventional NTGST, which shows the scalability of the proposed system implementation for the fast, high resolution computer vision inspection of the various sized BLUs in LCD production lines.

본 논문에서는 LCD (liquid crystal display) 생산라인에서 컴퓨터 비전에 의한 BLU (back light unit)의 고해상도 정밀검사를 원활하게 하기 위해 SIMD (single instruction stream and multiple data stream)형 병렬 구조의 다중 프로세서를 이용하여 계산 집약적인 NTGST (noise-tolerant generalized symmetry transform) 검사 알고리즘을 병렬구현 하였다. 먼저 알고리즘 자체의 속도향상을 위해 C 코드의 최적화를 거친 후, 순차형 프로그램을 N개의 데이터를 동시에 처리하는 SIMD형 언어로 변환하고, 검사영상 데이터를 SIMD형 다중프로세서에서 P개의 각 쓰레드에 분할 할당함으로써 O(NP)의 속도향상이 가능하도록 하였다. Dual Pentium Ⅲ 프로세서를 사용하여 실험한 결과, 제안한 병렬시스템은 기존보다 Sp=8 배 이상 고속 처리가 가능하여, 다양한 크기의 BLU에 대한 고해상도 정밀검사장비에도 신축적으로 확장적용 가능함을 확인하였다.

Keywords

References

  1. J. Jang, S. Lim, M. Oh, 'Technology Development and Production of Flat Panel Displays in Korea,' Proceeding of the IEEE, Vol. 90, No.4, Apr. 2002 https://doi.org/10.1109/JPROC.2002.1002522
  2. C. J. Park, S. H. Cho, and H. M. Choi, 'An Implementation of Noise-Tolerant Context-free Attention Operator and its Application to Efficient Multi-Object Detection,' lEEK Transaction on Signal Processing, vol. 38SP, no. 1, pp. 89-96, Jan. 2001
  3. C. J. Park, 'A Noise-Tolerant Context-free Attention Operator for Efficient Multi-Object Detection,' Ph. D. Thesis, Kyungpook National University, Daegu, Korea, 2000
  4. C. J. Park, W. G. Oh, S. H. Cho, and H. M. Choi, 'An efficient context-free attention operator for BLU inspection of LCD,' Proc. of the IASTED SIP, pp. 251-256, Las Vegas, Nevada, USA, Nov. 2000
  5. A. Peleg and U. Weiser, 'MMX Technology Extension to the Intel Architecture,' IEEE Micro, vol. 16, no. 4, pp. 42-50, Aug. 1996 https://doi.org/10.1109/40.526924
  6. S. K. Raman, V. Pentkovski, J. Keshava, 'Implementing streaming SIMD extensions on the Pentium III processor,' IEEE Micro, vol. 20, no. 4, pp. 47-57, July-Aug. 2000 https://doi.org/10.1109/40.865866
  7. D. Reisfeld, H. Wolfson, and Y. Yeshurun, 'Context-free attentional operators: The generalized symmetry transform,' IJCV, vol. 14, pp. 119-130, Jan. 1995 https://doi.org/10.1007/BF01418978
  8. A. Peleg, S.Wilkie, and U. Weiser, 'Intel MMX for Multimedia PCs,' Communications of the ACM, vol. 40, no. 1, pp. 25-38, Jan. 1997 https://doi.org/10.1145/242857.242865